The result is a predictable decay of a given set of customers over time, in the same way that uranium isotopes decay. There are three statistical analysis perspectives on this customer list decay.
You can focus on a set time period for decay, say 6 months, and ask “what is the probability that a customer acquired 6 months ago will leave in the next month.” You could train a static statistical model on the basis of training data, where each customer is classified 0/1 as to whether they left in the 7th month.
You can use basic survival analysis to understand the general survival rate over time of a typical customer, to use as a parameter in business planning. This would yield a function that you could use to determine risk for the typical customer at any given time.
You can add predictor variables to the survival model to model churn risk not just for a typical customer, but for a customer who fits a particular profile. This would allow more targeted interventions for a given customer to lower the probability of churning.